A Seminar
                                     on               Visit:
                                            http://...
Contents:
        • Introduction
        • History
        • Biological background
        • GAs
        • Example
       ...
Introduction:
• Genetic algorithms are a part of evolutionary
  computing, which is a rapidly growing area of
  artificial...
History:
• Evolutionary computing evolved in the
  1960’s by I. Rechenberg in his work
 "Evolution strategies"
• GA’s were...
Biological background:




Thursday, July 02,   Prakash B. Pimpale & Nitin S.   5
2009                            Bhande
Genetic algorithms:
   • Search space

   • Basic algorithm

   • Encoding

   • Crossover & Mutation

   • Selection on t...
Search space:

  -“The set of solutions among which the desired
  solution resides”.

 -With GA we look for the best solut...
Basic algorithm:
0 START : Create random population of n chromosomes
1 FITNESS : Evaluate fitness f(x) of each chromosome ...
Pictorial basic algo.




Thursday, July 02,   Prakash B. Pimpale & Nitin S.   9
2009                            Bhande
Encoding:
-“Coding of the population for evolution process”.

*Binary Encoding: In binary encoding, every chromosome is a
...
Encoding continued-
*Value Encoding: Values can be anything connected to
                            problem.
        Chro...
Crossover :
1.Single point crossover :          11001011+11011111 = 11001111




2.Two point crossover :            110010...
Mutation:
#Bit inversion: Selected bits are inverted

                                                 11001001 => 1000100...
Selection on the basis of
              fitness:
1.Rank selection: Ranks        the population first and then
every chromo...
2. Roulette Wheel Selection:

-The size of the section in the roulete wheel is
proportional to the value of the fitness fu...
3.Steady-State Selection:
-Allows big part of chromosomes to
  survive to next generation.
 -few good (with higher fitness...
4.Elitism:
- The best chromosomes are copied
  first & then the evolution proceeds
- This prevents loss of the best
  foun...
Example:




                               120


                               100


                                80
...
Coding for eight city problem:
Encoding used Permutation Encoding-

1) Aurangabad     2) Jalana       3) Parbhani
4) Manva...
Crossover-

Parent1 (1 2 8 7 5 6 4 3)
parent2 (2 8 7 1 3 4 5 6)

Child (1 2 8 7 3 4 5 6)



  Thursday, July 02,   Prakash...
Mutation-
Mutation involves reordering of the list:

                   *   *
Before:    (1 2 8 7 3 4 5 6)


After:      (...
Applications:
-Designing neural networks, both
  architecture and weights
-In Strategy planning
-TSP and sequence scheduli...
Advantages:
• Concept is easy to understand
• genetic algorithms are intrinsically
  parallel.
• Always an answer; answer ...
Limitations:
• The population considered for the
  evolution should be moderate or suitable
  one for the problem (normall...
Future scope:
• For finding new crossover methods
  for more convergence in single step
• For finding new mutation methods...
Conclusion:
-Genetic algorithms are rich - rich
  in application across a large and
  growing number of disciplines.
-Real...
References:
1)http://cs.felk.cvut.cz/~xobitko/ga/main.html
2)Genetic Algorithms and Evolutionary Computation
             ...
Thursday, July 02,   Prakash B. Pimpale & Nitin S.   28
2009                            Bhande
Upcoming SlideShare
Loading in...5
×

Genetic Algorithms Made Easy

14,745

Published on

This is an easy introduction to the concept of Genetic Algorithms. It gives Simple explanation of Genetic Algorithms. Covers the major steps that are required to implement the GA for your tasks.
For other resources visit: http://pimpalepatil.googlepages.com/
For more information mail me on pbpimpale@gmail.com

Published in: Technology
4 Comments
19 Likes
Statistics
Notes
No Downloads
Views
Total Views
14,745
On Slideshare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
1,370
Comments
4
Likes
19
Embeds 0
No embeds

No notes for slide

Genetic Algorithms Made Easy

  1. 1. A Seminar on Visit: http://pimpalepatil.googlepages.co m/ Genetic Algorithm by Prakash B. Pimpale & Nitin S. Bhande & Guided by: prof. Suhas S. Gajare Department of electronics & telecom. Engg. SGGS Institute of engg. & technology, Nanded. Thursday, July 02, Prakash B. Pimpale & Nitin S. 1 2009 Bhande
  2. 2. Contents: • Introduction • History • Biological background • GAs • Example • Applications • Advantages • Limitations • Future scope • Conclusion • References B. Pimpale & Nitin S. Thursday, July 02, Prakash 2 2009 Bhande
  3. 3. Introduction: • Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence. • Genetic algorithms (GA’s) are a technique to solve problems which need optimization • Based on idea that evolution represents search for optimum solution set • GA’s are based on Darwin’s theory of evolution Thursday, July 02, Prakash B. Pimpale & Nitin S. 3 2009 Bhande
  4. 4. History: • Evolutionary computing evolved in the 1960’s by I. Rechenberg in his work "Evolution strategies" • GA’s were invented by John Holland in the mid-70’s. • Jhon Holland wrote "Adaption in Natural and Artificial Systems" which was published in 1975. • In 1992 John Koza used GA’s for Genetic ProgrammingPimpale & Nitin S. Thursday, July 02, Prakash B. (GP) 4 2009 Bhande
  5. 5. Biological background: Thursday, July 02, Prakash B. Pimpale & Nitin S. 5 2009 Bhande
  6. 6. Genetic algorithms: • Search space • Basic algorithm • Encoding • Crossover & Mutation • Selection on the basis of fitness Thursday, July 02, Prakash B. Pimpale & Nitin S. 6 2009 Bhande
  7. 7. Search space: -“The set of solutions among which the desired solution resides”. -With GA we look for the best solution among among a number of possible solutions - represented by one point in the search space Possible solutions Search space Thursday, July 02, Prakash B. Pimpale & Nitin S. 7 2009 Bhande
  8. 8. Basic algorithm: 0 START : Create random population of n chromosomes 1 FITNESS : Evaluate fitness f(x) of each chromosome in the population 2 NEW POPULATION: 0 SELECTION : Based on f(x) 1 RECOMBINATION : Cross-over chromosomes 2 MUTATION : Mutate chromosomes 3 ACCEPTATION : Reject or accept new one 3 REPLACE : Replace old with new population: the new generation 4 TEST : Test problem criterium 5 LOOP : Continue step 1 – 4 until criterium is satisfied Thursday, July 02, Prakash B. Pimpale & Nitin S. 8 2009 Bhande
  9. 9. Pictorial basic algo. Thursday, July 02, Prakash B. Pimpale & Nitin S. 9 2009 Bhande
  10. 10. Encoding: -“Coding of the population for evolution process”. *Binary Encoding: In binary encoding, every chromosome is a string of bits - 0 or 1. Chromosome A 101100101100101011100101 Chromosome B 111111100000110000011111 *Permutation Encoding: In permutation encoding, every chromosome is a string of numbers that Chromosome A 1 5 3 2 6 4 7 9 8 represent a position in a sequence. Chromosome B 8 5 6 7 2 3 1 4 9 Thursday, July 02, Prakash B. Pimpale & Nitin S. 10 2009 Bhande
  11. 11. Encoding continued- *Value Encoding: Values can be anything connected to problem. Chromosome A 1.2324 5.3243 0.4556 2.3293 2.4545 Chromosome B ABDJEIFJDHDIERJFDLDFLFEGT Chromosome (back), (back), (right), (forward), (left) C *Tree Encoding: In this every chromosome is tree of some function or command in prog. language Chromosome A Chromosome B Thursday, July 02, ( + Prakash )B. Pimpale & Nitin S.( do_until step wall ) x (/ 5 y ) 11 2009 Bhande
  12. 12. Crossover : 1.Single point crossover : 11001011+11011111 = 11001111 2.Two point crossover : 11001011 + 11011111 = 11011111 3.Uniform crossover: 11001011 + 11011101 = 11011111 Thursday, July 02, Prakash B. Pimpale & Nitin S. 12 2009 Bhande
  13. 13. Mutation: #Bit inversion: Selected bits are inverted 11001001 => 10001001 #Order changing: Two numbers are selected and exchanged (1 2 3 4 5 6 8 9 7) => (1 8 3 4 5 6 2 9 7) Thursday, July 02, Prakash B. Pimpale & Nitin S. 13 2009 Bhande
  14. 14. Selection on the basis of fitness: 1.Rank selection: Ranks the population first and then every chromosome receives fitness value determined by this ranking. The worst will have the fitness 1, the second worst 2 etc. and the best will have fitness N (number of chromosomes in population). Situation before ranking (graph of fitnesses) Situation after ranking (graph of order numbers) Thursday, July 02, Prakash B. Pimpale & Nitin S. 14 2009 Bhande
  15. 15. 2. Roulette Wheel Selection: -The size of the section in the roulete wheel is proportional to the value of the fitness function of every chromosome - the bigger the value is, the larger the section is. -A marble is thrown in the roulette wheel and the chromosome where it stops is selected. Thursday, July 02, Prakash B. Pimpale & Nitin S. 15 2009 Bhande
  16. 16. 3.Steady-State Selection: -Allows big part of chromosomes to survive to next generation. -few good (with higher fitness) chromosomes are selected for creating new offspring -Then some bad (with lower fitness) chromosomes are removed and the new offspring is placed in their place -Thus process continues until we get optimal solution Thursday, July 02, Prakash B. Pimpale & Nitin S. 16 2009 Bhande
  17. 17. 4.Elitism: - The best chromosomes are copied first & then the evolution proceeds - This prevents loss of the best found solution - Elitism can rapidly increase the performance of GA Thursday, July 02, Prakash B. Pimpale & Nitin S. 17 2009 Bhande
  18. 18. Example: 120 100 80 60 y 40 20 0 0 10 20 30 40 50 60 70 80 90 100 x Graph of cities Thursday, July 02, Prakash B. Pimpale & Nitin S. 18 2009 Bhande
  19. 19. Coding for eight city problem: Encoding used Permutation Encoding- 1) Aurangabad 2) Jalana 3) Parbhani 4) Manvat 5) Nanded 6) Latur 7) Hingoli 8) Selu CityList1 (1 2 8 7 5 6 4 3) CityList2 (2 8 7 1 3 4 5 6) Thursday, July 02, Prakash B. Pimpale & Nitin S. 19 2009 Bhande
  20. 20. Crossover- Parent1 (1 2 8 7 5 6 4 3) parent2 (2 8 7 1 3 4 5 6) Child (1 2 8 7 3 4 5 6) Thursday, July 02, Prakash B. Pimpale & Nitin S. 20 2009 Bhande
  21. 21. Mutation- Mutation involves reordering of the list: * * Before: (1 2 8 7 3 4 5 6) After: (1 2 8 4 3 7 5 6) ->this is what the shortest path covering all cities! -so solution is as (1 2 8 4 3 7 5 6) Thursday, July 02, Prakash B. Pimpale & Nitin S. 21 2009 Bhande
  22. 22. Applications: -Designing neural networks, both architecture and weights -In Strategy planning -TSP and sequence scheduling -In game playing -Stock market predictions -In mathematics -And many more Thursday, July 02, Prakash B. Pimpale & Nitin S. 22 2009 Bhande
  23. 23. Advantages: • Concept is easy to understand • genetic algorithms are intrinsically parallel. • Always an answer; answer gets better with time • Inherently parallel; easily distributed • Less time required for some special applications • Chances of getting optimal solution are more » and many more Thursday, July 02, Prakash B. Pimpale & Nitin S. 23 2009 Bhande
  24. 24. Limitations: • The population considered for the evolution should be moderate or suitable one for the problem (normally 20-30 or 50-100) • Crossover rate should be 80%-95% • Mutation rate should be low i.e. 0.5%-1% assumed as best • The method of selection should be appropriate • Writing of fitness function must be accurate Thursday, July 02, Prakash B. Pimpale & Nitin S. 24 2009 Bhande
  25. 25. Future scope: • For finding new crossover methods for more convergence in single step • For finding new mutation methods for reducing divergence due to excess mutation • For finding a function which will decide suitable number of initial population Thursday, July 02, Prakash B. Pimpale & Nitin S. 25 2009 Bhande
  26. 26. Conclusion: -Genetic algorithms are rich - rich in application across a large and growing number of disciplines. -Really genetic algorithm changes the way we do computer programming. Thursday, July 02, Prakash B. Pimpale & Nitin S. 26 2009 Bhande
  27. 27. References: 1)http://cs.felk.cvut.cz/~xobitko/ga/main.html 2)Genetic Algorithms and Evolutionary Computation by Adam Marczyk 3)official site ofIllinois Genetic Algorithms Laboratory : http://www-illigal.ge.uiuc.edu/index.php3 4) Paper on Recentdevelopment in evolutionary & genetic algorithm: theory & applications. by N.Chaiyaratana & A.M.S.Zalzala 5)www.gwnetic~programming.com Thursday, July 02, Prakash B. Pimpale & Nitin S. 27 2009 Bhande
  28. 28. Thursday, July 02, Prakash B. Pimpale & Nitin S. 28 2009 Bhande
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×